Large multiple sequence alignments with a root-to-leaf regressive method.

Abstract:

:Multiple sequence alignments (MSAs) are used for structural1,2 and evolutionary predictions1,2, but the complexity of aligning large datasets requires the use of approximate solutions3, including the progressive algorithm4. Progressive MSA methods start by aligning the most similar sequences and subsequently incorporate the remaining sequences, from leaf to root, based on a guide tree. Their accuracy declines substantially as the number of sequences is scaled up5. We introduce a regressive algorithm that enables MSA of up to 1.4 million sequences on a standard workstation and substantially improves accuracy on datasets larger than 10,000 sequences. Our regressive algorithm works the other way around from the progressive algorithm and begins by aligning the most dissimilar sequences. It uses an efficient divide-and-conquer strategy to run third-party alignment methods in linear time, regardless of their original complexity. Our approach will enable analyses of extremely large genomic datasets such as the recently announced Earth BioGenome Project, which comprises 1.5 million eukaryotic genomes6.

journal_name

Nat Biotechnol

journal_title

Nature biotechnology

authors

Garriga E,Di Tommaso P,Magis C,Erb I,Mansouri L,Baltzis A,Laayouni H,Kondrashov F,Floden E,Notredame C

doi

10.1038/s41587-019-0333-6

subject

Has Abstract

pub_date

2019-12-01 00:00:00

pages

1466-1470

issue

12

eissn

1087-0156

issn

1546-1696

pii

10.1038/s41587-019-0333-6

journal_volume

37

pub_type

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